A Meta-learning approach for recommending the number of clusters for clustering algorithms

Author(s)

Pimentel, Bruno Almeida ^[1] ; de Carvalho, Andre C. P. L. F. ^[1]

Affiliation

^[1] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Sao Carlos - Brazil

• Document type: Journal article
• Source: KNOWLEDGE-BASED SYSTEMS; v. 195, MAY 11 2020.
• Web of Science Citations: 0

Abstract

One of the main challenges in Clustering Analysis is choosing the optimal number of clusters. A typical methodology is to evaluate a validity index over the data and to optimize it as a function of the number of clusters. However, this process can have a high computational cost. In this work, we introduce a new approach for recommending the number of clusters for a particular dataset by using Meta-learning. As the predictive performance of the meta-models induced by Meta-learning is affected by how datasets are described by meta-features, we propose a new set of meta-features able to improve the predictive performance of meta-models used for recommending the number of clusters. Experimental results show that the proposed approach provides a good recommendation of the number of clusters. Additionally, the proposed meta-feature obtains better results than meta-features for clustering tasks found in the literature. (C) 2020 Elsevier B.V. All rights reserved. (AU)

• FAPESP's process: 16/18615-0 - Advanced machine learning
• Grantee: André Carlos Ponce de Leon Ferreira de Carvalho
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
• FAPESP's process: 17/20265-0 - Use of meta-learning for clustering algorithm selection problems
• Grantee: Bruno Almeida Pimentel
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 12/22608-8 - Use of data complexity measures in the support of supervised machine learning
• Grantee: Ana Carolina Lorena
• Support Opportunities: Research Grants - Young Investigators Grants